What are the key differences between Artificial Intelligence (AI) and Machine Learning (ML)? If you’re confused by these terms, or even if you’re an expert on the subject, this guide will help you understand the basics of each and how they relate to each other.
What is AI?
AI is a broad term that encompasses many different technologies. AI is a machine’s ability to do things that would normally require human intelligence, such as understanding language, recognizing faces in images, or translating languages. ML is the use of algorithms to train AI algorithms on large data sets, so they can make predictions with minimal or no human input. It also includes a computer system’s ability to learn from new data without being explicitly programmed to do so.
The key differences between AI and ML
AI is the general term for a computer system that is designed to act more like a human in order to solve certain problems. ML focuses on using data in order to make predictions based on probabilities. The main difference between AI and ML is that AI uses data in order to learn, while ML uses data in order to make predictions. Another important difference between AI and ML is that AI deals with tasks such as decision-making or problem-solving, which can be difficult to automate.
How AI and ML are being used today
AI and ML are two of the hottest topics in technology. So how are they different? AI is more about self-thinking machines, while ML is a set of algorithms that can learn from data. The difference between the two is easiest seen through an example. Say you want a machine to look for faces on social media posts—a task that AI could do but an ML algorithm may not be able to do without human input. The AI would scan all posts and look for any faces, where as the ML would need someone to teach it what a face looks like so it could pick them out. The ML algorithm has also been shown to be less accurate than AI when it comes to facial recognition software used by law enforcement agencies.